Real-time process control for an immersed membrane filtration system using a control hierarchy of discrete-state parameter changes

ABSTRACT

An immersed membrane system or process may use measured or calculated process information to optimize one or more process operating parameters to improve performance or reduce operating costs. An on-line process control system or method may use the resistance in series method in operating an immersed membrane water treatment system. A process control system or process may consider resistance values and adjust operational parameters such as membrane aeration frequency factor, membrane aeration flow, permeate flux, permeation duration, backwash flow and duration, relaxation duration or maintenance or recovery chemical cleaning frequencies in order to reduce the operational costs related to membrane fouling removal.

This application is a continuation of U.S. application Ser. No.11/484,815 filed Jul. 11, 2006 which claims the benefit under 35 USC119(e) of U.S. Application Nos. 60/697,953 filed Jul. 12, 2005;60/697,974 filed Jul. 12, 2005; and, 60/751,979 filed Dec. 21, 2005.U.S. application Ser. Nos. 11/484,815; 60/697,953; 60/697,974; and60/751,979 are incorporated herein, in their entirety, by this referenceto them.

FIELD OF THE INVENTION

This specification relates to a membrane water treatment system orprocess, or to a process control system or method for a membrane watertreatment system, for example a membrane bioreactor or a system usingimmersed membranes.

BACKGROUND

The following is not an admission that anything discussed below is priorart or part of the general knowledge of people skilled in the art in anycountry.

Immersed membrane water treatment systems include, for example,wastewater treatment systems, such as membrane bioreactors, and waterfiltration systems, for example intended to produce potable water. Suchsystems may use air or other gases bubbled from under or between themembranes to scour the surface of the membranes to sustain the permeateflowrate for a given transmembrane pressure. The airflow rate istypically constant for a particular installation when expressed as avolume of air per unit membrane area per unit of time. For any of avariety of reasons, the ideal airflow rate at any moment can besignificantly different than the normal rate. These reasons may include:

a) changes in permeate flowrate and hence the loading rate of suspendedsolids onto the membrane surface;

b) changes in water viscosity:

-   -   i) in wastewater systems, whenever sludge is wasted, or if        equalization occurs in the membrane tank;    -   ii) in drinking water systems, if changes in coagulant dosage        are necessary because of changes in feed water composition or if        changes in recovery occur; or,    -   iii) in all systems if water temperature changes for example due        to seasonal variations;

c) changes in inlet blower air temperature or density which will affectthe mass of air delivered to the membranes for scouring; or,

d) in wastewater systems, changes in sludge filterability due to processchanges.

The permeate output from a water filtration system can vary for anynumber of factors. In municipal applications, factors include the timeof day, weather conditions and seasonal fluctuations. In industrialsystems, in addition to these factors, production schedules, strikes andplant shutdowns can result in changes in system output.

In wastewater treatment systems in particular (e.g. membranebioreactors), the influent flows can be highly variable and equalizationis generally provided by the system designer. In some installations,equalization is provided upstream of the membrane bioreactor in aseparate tank with transfer pumps and in other installations,equalization is provided in the membrane bioreactor tank. In allapplications, the viscosity and filterability of the biomass will varydue to process conditions. For example, after sludge is wasted from abioreactor and fresh feed is introduced, the suspended solidsconcentration will decrease. In those system designs whereinequalization is provided in the membrane bioreactor, the viscosity willchange as the feed flow to the membrane bioreactor varies. When thebioreactor liquid level is near its maximum, the viscosity will be thelowest and when the bioreactor is near its minimum, the viscosity willbe at its highest. Sludge filterability will change for any of a varietyof conditions including types of biological organisms present,production of extra cellular materials, pH, food to microorganismloading rates (F:M ratio), sludge age, and hydraulic retention time.

Membrane treatment systems consist of any number of separate blocks ofmembranes, referred to as trains or banks, which collectively producethe required total flow. The output from individual trains can vary asthe system output varies for the reasons described previously. Inaddition, the output from individual trains can be affected by otherfactors, in particular, the number of trains actually in service (sometrains may be out-of-service for maintenance or other reasons) and thedegree of fouling of the membranes (if severe enough to limit productionfrom an individual train).

In all membrane treatment applications, there can be defined a“suspended solids mass loading rate”. This rate reflects the rate atwhich suspended solids are brought to the membrane surface by the feedand is calculated as the “suspended solids concentration×the permeateflux” with units of “mass/unit membrane area−time”. At equilibriumconditions, the rate at which suspended solids are brought to themembrane surface has to equal the rate at which the turbulence and airscouring effects remove the suspended solids.

In control systems for currently manufactured immersed membrane systems,the practice is to set the aeration rate at a fixed rate based onstandard designs or pilot data. During commissioning, some manualoptimization may result in a change in aeration rates. Once thecommissioning is completed, adjustments to the aeration rates aregenerally not performed. The aeration rate (m3 of air per m2 of membranearea) is typically at or near the optimum aeration necessary whenoperating at full capacity or at the highest fluid viscosity and isconstant for all the trains in a system.

In immersed membrane treatment systems, the membrane filter is immersedin an open tank containing the solution of fluid to be filtered.Filtration is achieved by drawing water to the inside of membrane fiberunder a vacuum. The filtered water, also called permeate or filtrate, istransferred to a downstream tank, reservoir or receiving stream. Thematerials that do not pass through the membrane, including suspendedsolids, colloids and biological materials, are discharged as a solutioncalled the reject or retentate. This reject can be discharged eithercontinuously or intermittently depending on the system design. Air orother gases, under a slight positive pressure, are provided to theregion of the tank under or near the bottom of the membrane filters. Therising gas bubbles scour the membrane surface to reduce fouling andmaintain or slow a decline in permeation rate.

The productivity of an immersed membrane system is directly dependentupon many factors including: differential pressure across the membrane(also called transmembrane pressure), the membrane material and thewater's mass transfer rate through the boundary layer at the surface ofthe membrane. The rising air bubbles create turbulence and cause anupflow of water and the combination of turbulence and the upflow ofwater reduces the boundary layer thickness and increases the masstransfer rate through the boundary layer. The air can be suppliedcontinuously, cyclically (e.g. 10 seconds on, 10 seconds off) orintermittently (e.g. 60 seconds every 30 minutes). Energy is required toprovide this aeration and this can be a significant contributor to theoverall energy consumption of an immersed membrane system.

At the surface of the membrane filter, a “boundary layer” exists and allwater passing through the membrane must first pass through this boundarylayer prior to reaching the membrane's surface. This boundary layer isthe thin region at the surface of the membrane where a steep velocitygradient exists and the thinner the boundary layer, the steeper thevelocity gradient and the higher the mass transfer rate will be throughthe boundary layer. The thickness of the boundary layer varies with manyfactors including viscosity and the velocity of the fluid passing overthe surface and the concentration of the materials rejected by themembrane. The air supplied near bottom of the membrane inducesturbulence and the higher the air flow rate, the thinner the boundarylayer.

Membrane bioreactors (MBR) combine membrane technology and activatedsludge biodegradation processes for the treatment of municipal andindustrial wastewater. In MBR, immersed or external membranes are usedto filter the activated sludge from a bioreactor to produce a highquality effluent. Sample MBRs and their operation are described inInternational Publication No. WO 2005/039742 A1 which is incorporatedherein, in its entirety, by this reference to it.

The membranes may be generally arranged in modules or elements whichcomprise the membranes and the headers attached to the membranes and maybe formed together into cassettes and then trains. The modules areimmersed in a tank containing activated sludge. A transmembrane pressurein applied across the membrane walls which causes filtered water topermeate through the membrane walls. Solids are rejected by themembranes and remain in the tank to be biologically or chemicallytreated or drained from the tank for recycle or further treatment.

A typical treatment cycle comprises two stages. The first stage, knownas permeation, involves the production of membrane permeate through theapplication of transmembrane pressure, as described above.

The second stage involves the removal of solids from the membrane poresand surface. Two different operational procedures available arerelaxation and backwash. Relaxation is performed by eliminating thetransmembrane pressure which causes the permeate production to stop andallows for the air bubbles to remove the sludge particles deposited onthe membrane surface. The other operational procedure available forsolids removal is backwash. Backwash is performed by reversing thedirection of the permeate flow which allows for the removal of thesludge particles loosely deposited on the membrane pores and surface.

A cyclic air flow may be applied to the modules to minimize sludgeparticle deposition on the membrane surface. The cyclic aeration systemuses a valve set and a valve set controller to connect an air supply toa plurality of distinct branches of an air delivery network. Thedistinct branches of the air delivery network are in turn connected toaerators which may be located below the membrane modules. While the airsupply is operated to supply a steady initial flow of air, the valve setand valve controller split and distribute the initial air flow betweenthe distinct branches of the air distribution system such that the airflow to each branch alternates between a higher flow rate and a lowerflow rate in repeated cycles. The relative duration of periods of higherand lower flow rate applied to a given aerator are determined by theaeration frequency factor (A.F.F) which can be obtained by dividing thedurations of the period of higher air flow by the total duration of theaeration cycle (i.e. duration of higher air flow period plus duration oflower air flow period) respectively. In practical applications, valuesbetween 0.25 and 1 are common. For example, a system having fourbranches may be alternated between states of (a) providing aircontinuously to all four branches, (b) providing air cycles of 10seconds on and 10 seconds off by switching between pairs of thebranches, (c) providing a cycle of 10 seconds on and 30 seconds off byproviding air to each branch sequentially or (d) be at a continuous airoff state. The number of air blowers used in state (b) may be twice thatof state (c) and the number of air blowers in state (a) may be twicethat of state (b). An apparatus and method for providing cyclic air floware described in U.S. Pat. No. 6,550,747 which is incorporated herein,in its entirety, by this reference to it.

Air bubbles are introduced to the tank through aerators which may bemounted below or within the membrane modules and connected by conduitsto an air blower. The air bubbles rise to the surface of the membranetank and create an air lift which recirculates mixed liquor in the tankaround the membrane module. When the rate of air flow is within aneffective range, the rising bubbles and mixed liquor agitate themembranes to inhibit solids in the mixed liquor from fouling themembrane pores. Further, there is also an oxygen transfer from thebubbles to the mixed liquor which, in wastewater applications, providesoxygen for microorganism growth if desired.

Chemical cleanings may also be applied in order to remove those foulantsthat accumulate on the membrane pores despite the routine application ofbubbles, relaxation or backwash. Maintenance chemical cleaning, whichrequires a less concentrated chemical solution, may be applied tomaintain or reduce a rate of decline in membrane permeability. Recoverychemical cleaning, which requires a more concentrated chemical solution,may be applied at a lower frequency to restore membrane permeabilitywhen it has fallen considerably.

Membrane fouling is probably the most common operational problemencountered in MBR. Membrane fouling occurs when membranes pores areobstructed resulting in the loss of membrane permeability, which is thevolume of permeate that can be passed through a membrane surface perunit of pressure or vacuum applied.

The complex mechanisms behind membrane fouling have been widely studiedin recent years.

Membrane fouling is highly influenced by diverse MBR operationalparameters such as influent wastewater temperature, membrane aerationfrequency factor, membrane aeration flow; permeate flux, permeationduration, backwash flow and duration, relaxation duration, maintenanceand recovery chemical cleaning frequencies.

The resistance in series method has been used for membrane foulingquantification and identification of the main fouling mechanism (i.e.pore blocking, cake filtration) at any given set of operationalconditions. This method allows for a detailed breakdown andquantification of membrane fouling which makes it possible to identifythe causes of membrane fouling.

As it has been previously described, there are several operationalalternatives for fouling removal available in MBR such as relaxation,backwash, maintenance and recovery chemical cleaning. The application ofeach of these methods is aimed at the removal of different kinds offouling. Relaxation and backwash are designed to mechanically remove thefoulants deposited on the membrane surface or loosely inserted into themembrane pores. On the other hand, maintenance and recovery chemicalcleaning are meant to chemically remove the foulants deeply adsorbedinto the membrane pores and biofilm strongly attached to the membranesurface.

Ideally the decision for the application of any of these differentfouling removing methods as well as the remaining MBR operationalparameters is preceded by a detailed analysis of the membrane foulingand the identification of the main fouling mechanism. However, thisanalysis, if done at all, is based on off-line data and takes placesporadically or only during piloting or start up. Currently, MBR processcontrol is limited and lacks flexibility to adjust to the differentoperational conditions encountered in practice. The operational changesare made manually from off-line data and infrequently, if at all, andare highly dependent on the skill and good judgment of the operator.

SUMMARY

The following summary is intended to introduce the reader to theinvention but not define it. The invention may reside in any combinationof one or more process steps or apparatus elements selected from the setof every element and step described in any part of this document. Theinventors do not waive or disclaim their rights to any invention orinventions disclosed in this specification merely by not describing suchother invention or inventions in the claims.

This specification describes an immersed membrane treatment system thatuses real-time process information to adjust the instantaneous or timeaveraged scouring airflow rate or other alterable parameters. A processis also described for adjusting the supply of scouring air to animmersed membrane using process information. Feedback control to theactual air supply equipment can be automatic (via PLC or computer) ormanual (for example an operator initiates a change in air deliveryrate).

Various tests have been conducted to determine the optimum aeration ratefor various applications, e.g. wastewater treatment, direct filtrationof surface waters and filtration of waters pretreated with coagulant orother chemicals. These results, or other pilot results, serve as thebasis for designing the blower capacities and aeration rates infull-scale systems. It has been successfully demonstrated that, at aparticular transmembrane pressure, an increase in aeration rate canresult in an increase in permeate flow. Under these conditions, masstransfer through the boundary layer may be rate-limiting. Optimizationof the boundary layer thickness through adjustments in airflow providesa method of reducing operating energy requirements.

If the aeration rate is less than the optimum, the mass transfer throughthe boundary layer can have a significant affect and reduce the permeateoutput of the system. If the aeration rate is higher than the optimum,energy is being wasted as excess air is being supplied. To maximizeproduction from a system and to minimize operating costs requirescontrol of the aeration rate as process conditions vary.

This specification describes a system or process wherein aerationefficiency is improved to help reduce operating energy and operatingcost. Airflow, for example at constant rate or a time averaged rate, iscontrolled to account for real-time differences in process conditionse.g. permeate flows, feedwater viscosity, inlet air temperature, watertemperature or coagulant dosage. This invention enables the controlsystem, with information from process instrumentation, to determine adesired aeration rate or change in aeration rate predicted to improveproduction or reduce cost. The actual airflow can then be adjustedeither automatically by the control system or manually by the operatordepending on the installation. The rate adjustment may occur byadjusting the rate of continuous aeration or by adjusting aerationon-off times (cyclic or intermittent aeration), train on/off times ornumber of trains in operation.

Methods of controlling a system may include one or more of

-   -   a) Measuring the performance of the system over a relatively        short period of time (e.g. 15 to 60 minutes), comparing those        results with baseline values or values from a previous or        preceding period of time and using that information to adjust        any of the following:        -   the actual air flow rate delivered to the membrane over a            period of time        -   aeration on/off times when operating in cyclic or            intermittent aeration modes        -   train on/off times        -   number of trains in operation and design permeate flow per            train    -   b) Obtaining real-time process information and adjusting the        airflow rate or other variables listed in a) above based on        model data previously incorporated into the control system.        Some processes may be used with all immersed membrane systems,        including direct filtration and wastewater systems.

In another aspect, this specification describes an on-line, processcontrol system or method using the resistance in series method, that maybe used for operating microfiltration or ultrafiltration immersed MBR orother membrane treatment systems. This process control may consideron-line resistance data in adjusting operational parameters such asmembrane aeration frequency factor, membrane aeration flow, permeateflux, permeation duration, backwash flow and duration, relaxationduration, or maintenance or recovery chemical cleaning frequencies inorder to optimize the operational costs related to membrane foulingremoval. The means to control may be, for example, feedback, feedforward, adaptive or model predictive.

The on-line, process control system consists of sensors, dataacquisition, a controller and signal conditioning accessories, ifrequired. The data acquisition and signal conditioning accessories areresponsible for collecting and conditioning the system operational data(e.g. permeate temperature, transmembrane pressure and permeate flux,etc) while the controller is in charge of analyzing the stream ofon-line operational data using the resistance in series method,calculating control parameters (for example, one or more of R_(a),ΔR_(ab) and R_(c)), comparing the obtained results with correspondingset points and making the decision of how to modify the systemoperational parameters to reduce operational costs related to membranefouling removal. The operational parameters may be modified on a stepwise manner, one at a time, following a control logic that givespriority to those operational changes that produce a significant impacton membrane fouling removal over those that produce a limited effect.The control logic presented below can be modified according to theoperational conditions encountered in practice.

For those cases where a value of resistance is larger than anestablished high limit set point and there is a need to reduce themembrane fouling rate (Fouling Removal mode), a single parameter may bechanged, for example membrane aeration flow rate or frequency factor mayincrease. Alternatively, a control hierarchy may be establishedincluding one or more of the following:

1. Switch from relaxation to backwash, if backwash is not the currentlyused operational mode.

2. Increase backwash flow rate.

3. Reduce the permeate flux by turning on membrane trains.

4. Increase the membrane aeration flow rate.

5. Increase the membrane aeration frequency factor.

6. Add activated sludge filterability enhancer such as a polymer orferric chloride.

7. Increase to maximum available aeration frequency factor.

8. Increase the maintenance chemical cleaning frequency.

9. Increase the recovery chemical cleaning frequency.

For those cases where stable operational conditions have been reachedand the value of every measured resistance is equal or less than anestablished low limit set point, there is an opportunity to decreasesystem operational costs (Energy Savings mode). A single parameter maybe altered, for example membrane aeration flow rate or frequency factormay decrease. Alternatively, a control hierarchy may be established asfollows:

1. Decrease aeration frequency factor from its maximum available value.

2. Stop the addition of activated sludge filterability enhancer.

3. Decrease the membrane aeration frequency factor.

4. Decrease the membrane aeration flow rate.

5. Increase the permeate flux by turning off membrane trains.

6. Decrease backwash flow, if backwash is the currently used operationalmode.

7. Switch from backwash to relaxation, if relaxation is not thecurrently used operational mode.

8. Decrease the maintenance chemical cleaning frequency.

9. Decrease the recovery chemical cleaning frequency.

Changing these parameters can be done, for example, by an incremental orpredicted effective amount within a range of values permitted by thesystem. Optionally, each element of the control hierarchy may have 2 ormore discrete states. The states differ in their effectiveness againstfouling. To reduce fouling or fouling rates, the control hierarchy maymove one or more controlled parameters to the more effective statestarting from the top of the hierarchy until acceptable operationalconditions, for example as determined by comparing one or moreresistance values to one or more set points, are achieved. To provide anenergy savings, the control hierarchy may move one or more parameters toa less effective state starting at the top of the hierarchy until thedesired performance is achieved. While some parameters may optionally beinfinitely variable, the inventors have found that many parameters haveonly very small ranges in which they are variable in a real system andproviding two or more discrete states may provide more effectivecontrol. Air scouring, for example, is surprisingly difficult to vary.Air blowers that feed aerators tend to operate efficiently only in asmall range of speeds. Aerators also sludge up or produce poorly sizedbubbles if feed air flow rate is outside a narrow range. However, A.F.F.can be varied, for example between two discrete states such as 0.5 and0.25, by changing the operation of a valve set and turning one or moreof a set of blowers on or off, for example turning half of the blowersoff when A.F.F. is changed from 0.5 to 0.25. The two states differmarkedly in effect on fouling but blowers and aerators can operate wellin both states. A control hierarchy may have more or less parametersthan the hierarchy described above, and the parameters may be in otherorders. Where a parameter in the hierarchy does not have discretestates, its value may be changed to an upper or lower limit beforemoving to the next parameter in the hierarchy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of an immersed membrane treatmentsystem with a control system according to an embodiment of the inventionor adapted to carry out a process according to the invention.

FIG. 2 is a decision tree for a control system.

FIG. 3 is another decision tree for a control system.

FIG. 4 is a graph of experimental results.

FIG. 5 is a schematic plan view of a membrane tank showing thearrangement of aerators.

FIG. 6 is a schematic drawing of parts of an air supply system.

DETAILED DESCRIPTION

Various apparatuses or processes will be described below to provide anexample of an embodiment of each claimed invention. No embodimentdescribed below limits any claimed invention and any claimed inventionmay cover processes or apparatuses that are not described below. Theclaimed inventions are not limited to apparatuses or processes havingall of the features of any one apparatus or process described below orto features common to multiple or all of the apparatuses describedbelow. It is possible that an apparatus or process described below isnot an embodiment of any claimed invention. The applicants, inventorsand owners reserve all rights in any invention disclosed in an apparatusor process described below that is not claimed in this document and donot abandon, disclaim or dedicate to the public any such invention byits disclosure in this document.

Referring to FIG. 1, a system 8 includes an immersed membrane watertreatment system 10 which is connected to one or more sensors 12 whichprovide input information to a computer 14 and optionally to an operatorthrough an operator interface 16. The specific process inputs providedby the sensors 12 can include one or more of the following:

Permeate flow

Reject or concentrate flowrate

Sludge filterability

Fluid temperature

Fluid viscosity

Fluid suspended solids concentration

Coagulant or other pretreatment chemical addition rate

Activated carbon dosage rate or concentration

Blower inlet air temperature

Recovery rate (permeate produced/feedwater provided)

Dissolved oxygen concentration (wastewater systems)

Oxygen uptake rate (wastewater systems)

Solids retention time (wastewater systems)

Mixed liquor recirculation rates (wastewater systems)

After considering the information from the sensors 12, the computer 14,after running its own programs or as instructed by an operator throughthe interface 16, instructs process controllers 18 to adjust theaeration regime of the system 10.

The methods or step which the process controllers 18 may program may beone or more of:

-   -   a. Adjustments to the airflow rate (continuous, cyclic or        intermittent)        -   Example: reduce instantaneous airflow from 0.35 m³/h-m² to            0.30 m³/h-m²    -   b. Adjustments to the on and off aeration times during cyclic or        intermittent aeration cycle,        -   Examples: change cyclic aeration times from “10 seconds            on/10 seconds off” to “10 seconds on/20 seconds off”; or            change intermittent aeration times from “1 minute every 30            minutes” to “1 minute every 45 minutes”    -   c. Adjustments to the on and off production times for an        individual train, or        -   Example: change from “18 hours on/6 hours off” to “3 hours            on/1 hour off”    -   d. Adjustments to the number of trains in operation or design        permeate flow for each train as part of a complete system.        -   Example: change from 2 systems operating at a higher flux to            3 systems operating at a lower flux (for example when            filterability of sludge changes)

Methods by which the sensors 12 may be used to collect real-time processmeasurements that can be used to determine changes in the aerationregime may include one or more of the following:

A method may involve steps of testing, evaluating results and thenoptimizing. For example, process controllers 18 may operate to provide afixed aeration rate for a period of time to establish baseline processoperating conditions (including flux, transmembrane pressure). Thisbaseline data may be conducted, for example over 4 to 8 filtrationcycles, approximately 2 hrs. The system 8 would then automaticallyconduct a test on one or more of the trains. In this test, it wouldmaintain the same permeate production flow but adjust the aeration rateupward or downward by a certain percentage, e.g. 5%, for as manyfiltration cycles as necessary to obtain steady state performance. Thesystem 8 would monitor the process conditions (in particular TMP andfouling rate) and would be able to determine when steady stateconditions are achieved. Since the development of the new boundary layeris expected to be extremely fast, steady state is expected to be 2 or 3filtration cycles. The specific process conditions will be evaluated bythe system 8 to determine if the change has affected aerationefficiency. Knowing the efficiency of the blowers and the permeatepumps, the system 8 can determine if the new aeration rate yields betterperformance in terms of either simple permeate flow or total energyefficiency (flowrate per unit energy input). If a decrease in bloweroutput does not change the permeation TMP, it can be concluded that theaeration rate was in excess of that necessary to remove the suspendedsolids loaded on to the membrane. If a decrease in aeration rate resultsin an increase in TMP, then the control system can quantify the changesin operating costs and assess if the change was positive or negative.Once the analysis of the results is complete, the system can conduct asimilar test at a new aeration rate. This test may be repeated until theoptimum aeration rate has been determined.

The system would continue to operate at the set aeration rate untilchanges in process operating conditions were noted e.g. an increase inthe permeate flow setpoint would result in the optimization tests beingrepeated. Optimization tests could also be triggered based on a timeinterval (e.g. every 6 hours or once a day) or whenever initiated by theoperator (e.g. after sludge wasting has occurred).

In a method using an on-line filterability test, a train of membranefilters is operated under controlled conditions for a short period oftime to test the filterability of the fluid being filtered. The testconsists of three steps:

-   -   1. Stop membrane permeation but continue aeration for a period        of 30 seconds to 5 minutes. The purpose of this step is to        deconcentrate the liquid surrounding the membranes and to reduce        the boundary layer thickness to a baseline level.    -   2. Stop aeration and start permeation of the membrane filters at        a specified membrane flux for a period of 30 seconds to 20        minutes. During this time, the transmembrane pressure will rise        due to the redevelopment of the boundary layer at the surface of        the membrane filters. The purpose of this step is to develop a        relationship between transmembrane pressure and time. This        relationship can be correlated to an optimum aeration rate.    -   3. Stop membrane permeation and resume aeration for a period of        30 seconds to 5 minutes. The purpose is to remove the solids        that accumulated on the membranes during step 2.    -   4. Based on the results of the test, and comparing the results        with previously input model results, adjust aeration rate or        operating strategy (refer to methods a) to d) described above        and the train is ready to operate under new optimized        conditions.

In a process parameter modeling method, the control system would usereal-time data to determine the membrane system operating conditions andbased on those conditions and previously input models, would set theaeration flow or system operating strategy accordingly. The optimizationcan be as simple as tracking a single parameter (e.g. as permeate flowincreases, so does aeration rate) or as many parameters as is necessarycan be used. For example, in wastewater treatment systems, the optimumaeration rate could be a function of the sludge filterability andpermeate flowrate. A model is first developed and the data incorporatedinto the system 8. The models necessary will depend on each applicationand the parameters that can be expected to change with operation.Another example would be if the sludge filterability is good, 2 trainscan be operated at a higher flow and when the sludge filterability islow, three trains can be operated at proportionately lower flows.

A method may use measured or calculated resistance data, for example oneor more of the resistance parameters in the resistance in series model.The resistance in series model represents the total resistance tofiltration as the combination of a number of independent resistances.Resistance values may be used in, for example, feedback, feed forward,adaptive or model predictive control processes.R _(t) =R _(m) +R _(a) +R _(b) +R _(c)

R_(t) is the total resistance, m⁻¹

R_(m) is the membrane resistance, m⁻¹ as represented by the membraneclean water test

R_(a) is the adsorptive fouling resistance, caused by fouling agentsadsorbed on the surface and in the porous structure of the membrane.

R_(b) is the pore blocking resistance, m⁻¹, caused by colloidal matterand micro particles that are comparable in size to the membrane poresize. This is normally represented by a rapid rise in transmembranepressure immediately following a backwash.

R_(c) is the cake resistance, m⁻¹ which progressively increases betweentwo backwash cycles as the cake builds up on the membrane surface.

For this particular application, the value of R_(m) is considered asconstant and can be obtained from a clean water test. The resistanceduring backwash (R_(db)) will be used to calculate the adsorptivefouling resistance R_(a) and the membrane resistance as follows:R _(a) =R _(db) *f−R _(m)

Where f is a factor that relates resistance during permeation to theresistance during backwash.

The cake resistance (R_(c)) will be estimated by performing a linearregression for those experimental data of a given permeation cycle thatcorresponds to the permeation mode and comply with one or more of thefollowing conditions, called Valid Permeation Cycle (VPC) conditions.J=J _(setpoint)±35%;

where J_(setpoint) is the permeate pump set point. Alternate oradditional criteria may also be used for VPC conditions. For example,the VPC conditions may be that J=J_(setpoint)±35% and dR_(t)/dT<5% for 5consecutive samples. dR_(t)/dT is the rate of increase of totalresistance in time during permeation and it is calculated as:dR _(t) /dT=(R _(n+1) −R _(n))/(T _(n+1) −T _(n))

Where R_(n) and R_(n+1) are two consecutive total resistance data pointsof any given permeation cycle and T_(n) and T_(n+1) are thecorresponding permeation times.

Then the cake resistance will be calculated using the experimental datathat complies with the above illustrated conditions.R _(c) =M(T ₂ −T ₁)

Where M is the slope of the linear regression; T₁ and T₂ are thestarting and ending times of the permeation cycle; respectively.

The pore blocking resistance will be estimated as follows:R _(b) =R _(ab) *e−R _(m)

Where e is the cake removal efficiency and represents the amount of cakeremaining on the membrane surface after the application of relaxation orbackwash and is a function of the cake stickiness and the operationalconditions of the relaxation or backwash. R_(ab) is the resistance afterbackwash and is determined by the average of the first five samples of apermeation cycle that meet the VPC condition as outlined in the cakeresistance calculation.R _(ab)=(R _(t1) +R _(t2) +R _(t3) R _(t4) +R _(t5))/5

The first step in the control strategy involves the calculation of thefiltration resistances during operation at permeation mode using on-lineMBR operational data, in a similar fashion as described above.

The calculation of the resistance after backwash (R_(ab)), resistanceafter backwash increase rate (ΔR_(b)) and cake filtration (R_(e))resistances is of particular interest for the adjustment of the value ofthe membrane aeration frequency factor, membrane aeration flow,relaxation cycle duration, permeation cycle duration, backwash flow,backwash duration to reduce the membrane fouling rate, using an on-lineprocess control. The calculation procedure is as follows:

Measure TMP, Permeate Flux, Temperature and Time for two consecutivepermeation and backwash or relaxation cycles.

Calculate ΔR_(ab) and R_(c) for any given permeation cycle as follows:ΔR _(ab) =R _(ab)(cycle 2)−R _(ab)(cycle 1)  i.R _(c) =M(T ₂ −T ₁)  ii.

Compare ΔR_(ab) and R_(c) values with their corresponding set points(Table 1.0) to adjust the value of the membrane aeration frequencyfactor, membrane aeration flow, relaxation cycle duration, permeationcycle duration, backwash flow and duration, maintenance and recoveryclean frequencies so as to minimize the energy required for membranefouling removal. The corresponding operational changes may be performedevery cycle, if needed.

The calculation of the adsorptive resistance (R_(a)) is optional but maybe of particular interest for the adjustment of the value of themaintenance and recovery chemical cleaning frequencies using an on-lineprocess control, if desired. However, calculation of R_(a) may also beomitted if chemical cleaning procedures will not be controlled. Thecalculation procedure is as follows:

1. Initiate a filtration cycle (1) followed by backwash period followedby another filtration cycle (2). In those cases where relaxation is usedas the mechanism for cake removal it would be required to switch tobackpulse mode after a number of cycles in order to collect theinformation needed to estimate the membrane condition.

2. Measure TMP, Permeate Flux, Temperature and Time for two consecutivefiltration and backwash cycles.

3. Calculate (R_(a)) as follows:(R _(a))=resistance during backwash (cycle 2)−resistance during backwash(cycle 1)

4. Compare (R_(a)) values with its corresponding set point to adjust thevalue of the maintenance and recovery chemical cleaning frequencies asto minimize the energy required for membrane fouling removal. Thecorresponding operational changes will be performed after five cycles,if needed.

Two different groups of set points may be established for a particularsystem; sustainable and optimized. The values of these set points mightchange for different treatment plants as they take into accountdifferent operational variables (e.g. mixed liquor characteristics,wastewater temperature) that are known to be site related. The setpoints may be determined during piloting of a system or based onhistorical system performance. One or more of the set points may alsovary with time. The different resistances that are being monitored, forexample ΔR_(ab) and R_(c), will be compared against their respective setpoints in order to perform the adequate operational changes. Thecalculated resistances may be single values or a composite of severalvalues spaced in time, for example as obtained by a mathematicalaveraging or regression.

The sustainable set point represents the maximum value of resistance atwhich the system should be operated. When any of the values of theresistances being monitored are higher than any of the correspondingsustainable set points (Red Zone); only those operational changes willbe made that ensure that a reduction in the membrane fouling is achieved(Fouling Removal mode).

On the other hand, the optimized set point is the maximum value forwhich operational changes to achieve energy savings are possible; whenany of the values of the resistances being monitored are between thesustainable and the optimized set point (Yellow Zone), no change of theoperational parameters will be performed. When all of the values of theresistances being monitored are lower than the values of thecorresponding optimized set point (Green Zone), then it is possible toexecute operational changes that lead to energy savings (Energy Savingsmode).

If one of the set points is exceeded, then the system will be consideredto be operating at the zone corresponding to that set point. The two setpoints enhance the stability of the process, help avoid switching systemoperations too frequently or in response to errant resistancemeasurements and allow for operation for extended periods of time withina range between the two set points. However, a single set point can beused if the process is otherwise dampened. For example, more robustregression algorithms can be used on the calculated resistances, amathematical band may be constructed around the set point, the samplingrate may be decreased, the calculated resistances may be required to beabove or below the set point at multiple sampling periods or othertechniques or combinations of techniques can be used to dampen thesystem.

FIGS. 2 and 3 show decision trees for systems having a single parameter,A.F.F., adjustable between two operational states. The two states are 10seconds on 10 seconds off and 10 seconds on 30 seconds off. In the 10/10cycle, a number of air blowers are used and the total air supply iscycled between two halves of a number of aerators. In the 10/30 cycle,half of the air blowers are turned off and the total air is cycledbetween four quarters of the number of aerators. For example, FIG. 5shows a membrane tank 30 having several conduit aerators 32 eachconnected to one of two tank manifolds 34 running along the bottom ofthe tank 30. Tank manifold 34 a delivers air to one half of aerators 32while tank manifold 34 b delivers air to the other half of aerators 32.Drop legs 36 extend upwards from tank manifolds 34 and allow air to befed from above the tank 30 to the grid of aerators 32. Referring to FIG.6, 4 tanks 30 each contain a number of immersed membrane cassettes 38placed over the grid of aerators 32. The cassettes 38 are connectedtogether for permeate removal and may be called a train. Aerators 32,tank manifolds 34 and drop legs 36 are not shown in FIG. 6 but are alsopresent in each tank 30 of FIG. 6 as shown in FIG. 5. The drop legs 36from each tank 30 connect to connectors 40 of supply system 42. Supplysystem 42 further includes a set of valves 44 in pipes 46 betweenconnectors 40 and a plant manifold 48. Plant manifold 48 is connected tofour blowers 50. Valves 44 are powered by solenoids of fluidic actuatorsand connected to process controllers 18. During 10/10 aeration, all fourblowers 50 are on, and valves 44 are controlled such that valves 44a,c,e,g are open for 10 seconds while valves 44 b,d,f,h are closed thenvalves 44 a,c,e,g are closed for 10 seconds while valves 44 b,d,f,h areopen. This cycle is repeated for as long as the 10/10 cycle isrequested. Thus, each cassette always has a flow of air to it, but theflow switches between aerators 32 connected to the different tankmanifolds 34. When a 10/30 cycle is requested, two trains are linkedsuch that air flow is alternated between the two tank manifolds 34 ineach tank 30 and between the two tanks 30, that is between four tankmanifolds 34 in two tanks 30. Thus each cassette 38 experiences a 10seconds on 10 seconds off aeration pattern but with the source of air inconsecutive air on periods alternating between the two sets of aerators32 in that tank 30. Each aerator 32 has a 10 seconds on 30 seconds offpattern. In greater detail, for 10 seconds valves 44 a,e are open whilethe others are closed. For the next 10 seconds, valves 44 c,g are openwhile the others are closed. For the next 10 seconds, valves 44 b,f areopen while the others are closed. For the next 10 seconds, valves 44 d,hare open while the others are closed. This pattern is repeated for aslong as 10/30 aerations is requested. Other methods of reducing a timeand space averaged air flow without reducing instantaneous flow toindividual aerators might also be used. For example, in a system whereair is supplied continuously or cyclically or intermittently from a setof blowers to a set of aerators, half of the blowers may be turned offand a valve closed to isolate half of the aerators. Although thediscussion above describes two or three types of resistance values, one,two, three or more than three resistance values may be used. Forexample, in the system of FIG. 2, cake resistance may be the onlyparameter used since it is closely related to air scouring.

EXAMPLES Example 1

In a 2 month test period the application of an on-line MBR processcontrol, based on the results from the resistance in series method wasstudied. This on-line MBR process control will adjust differentoperational parameters (e.g. membrane aeration frequency factor,relaxation duration, etc.) to reduce the MBR operational costs orincrease membrane fouling removal, as required.

A ZeeWeed® immersed membrane pilot plant, made by ZENON EnvironmentalInc, was operated using raw wastewater feed from a municipal waterpollution control centre. The raw wastewater was screened through a 0.75mm screen. The pilots were operated at a hydraulic retention time of 6hours and a sludge retention time of 15 days.

At the first set of conditions that lasted 2 days, ZeeWeed® membraneswere operated for a 10 minute permeation cycle with a net flux of 14gallons/(square foot*day) (gfd), a corresponding instantaneous flux of15.4 gfd and 1 minute relaxation time. The systems were operated at amixed liquor suspended solids concentration of around 10 g/l.

The membrane aeration frequency factor was set at 0.25 (10 seconds onand 30 second off) during permeation and relaxation; a coarse air bubbleflow rate of 8 scfm per gap was used. The bioreactor was aerated usingfine bubble aerators. Mixed liquor was recirculated from the bioreactorto the membrane tank by a pump and was returned to the bioreactor bygravity. Sludge was wasted on intermittent basis to maintain a steadysludge retention time.

A detailed analysis of the characteristics of this MBR system allowedfor the identification of the corresponding on-line MBR process controlset points for this system; these are presented in Table 1.0. The yellowzone is an operation zone between the red zone and the green zonealthough it has no distinct operation mode name or parameters.

TABLE 1.0 Set points of the MBR on-line process control. OperationParameter zone Set point Operation mode ΔR_(ab) R_(c) R_(a) RedSustainable Fouling Removal 2*10⁸ 8*10¹¹ 2*10⁸ Green Optimized EnergySavings 1*10⁸ 6*10¹¹ 1*10⁸

Tables 1.1 and 1.2 contain some of the operational data corresponding totwo consecutive permeation cycles of operation under the above describedoperational conditions. These operational data will be used to describethe resistance calculations.

TABLE 1.1 Permeation cycle 1 experimental data (experimental conditions1). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 08:50:41 0.00.04 12.73 0.0 N/A N/A 08:50:46 0.0 0.02 12.73 0.0 N/A N/A 08:50:51 0.00.03 12.72 0.0 N/A N/A 08:50:56 0.0 0.03 12.72 0.0 N/A N/A 08:51:01 0.00.03 12.68 0.0 N/A N/A 08:51:07 0.0 0.03 12.69 0.0 N/A N/A 08:51:12 0.00.03 12.69 0.0 N/A N/A 08:51:17 0.0 0.01 12.70 0.0 N/A N/A 08:51:22 0.0−0.07 12.71 0.0 N/A N/A 08:51:27 0.0 0.02 12.72 0.0 N/A N/A 08:51:32 0.0−0.02 12.74 0.0 N/A N/A 08:51:37 0.0 0.84 12.73 0.0 N/A N/A 08:51:42 81.39 12.72 0.0 2.57E+12 6 08:51:47 15.5 2.90 12.70 0.0 2.73E+12 208:51:52 15.6 2.96 12.71 0.0 2.79E+12 0 08:51:57 15.5 2.99 12.70 0.02.79E+12 1 08:52:02 15.7 2.99 12.69 0.0 2.81E+12 1 08:52:07 15.6 3.0112.68 0.0 2.83E+12 0 08:52:12 15.7 3.02 12.70 0.0 2.83E+12 −1 08:52:1715.7 3.03 12.71 0.0 2.81E+12 0 08:52:22 15.4 3.03 12.72 0.0 2.79E+12 108:52:27 15.5 3.00 12.71 0.0 2.83E+12 0

TABLE 1.2 Permeation cycle 2 experimental data (experimental conditions1). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 09:01:420.000 0.09 12.65 0.001 N/A N/A 09:01:47 0.000 0.06 12.64 0.001 N/A N/A09:01:52 0.000 0.04 12.63 0.001 N/A N/A 09:01:57 0.000 0.05 12.62 0.001N/A N/A 09:02:02 0.000 0.04 12.63 0.001 N/A N/A 09:02:07 0.000 0.0412.63 0.001 N/A N/A 09:02:12 0.000 0.16 12.64 0.001 N/A N/A 09:02:170.000 0.03 12.65 0.001 N/A N/A 09:02:22 0.000 −0.01 12.66 0.001 N/A N/A09:02:27 0.000 0.02 12.65 0.001 N/A N/A 09:02:32 0.000 0.02 12.65 0.001N/A N/A 09:02:37 0.000 0.02 12.64 0.001 N/A N/A 09:02:42 4.753 0.7712.64 0.001 2.37E+12 15 09:02:48 9.888 1.89 12.64 0.001 2.80E+12 109:02:53 15.777 3.03 12.63 0.001 2.83E+12 2 09:02:58 15.550 3.08 12.640.001 2.90E+12 3 09:03:03 15.410 3.20 12.64 0.001 2.97E+12 0 09:03:0815.498 3.09 12.65 0.001 2.98E+12 −5 09:03:13 15.593 3.09 12.66 0.0012.84E+12 2 09:03:18 15.601 3.06 12.66 0.001 2.89E+12 0 09:03:23 15.6673.10 12.65 0.001 2.89E+12 1 09:03:28 15.550 3.12 12.64 0.001 2.93E+12 009:03:33 15.615 3.13 12.64 0.001 2.93E+12 2 09:03:38 15.396 3.13 12.640.001 2.99E+12 0

The operational data corresponding to these conditions was used tocalculate the different relevant resistances.

As mentioned above, the resistance after backwash will be calculatedbased on the average of the first five values that comply with the validpermeation cycle conditions. The adsorptive fouling resistance was notcalculated in the following examples.

As it can be observed from the presented table, for the first permeationcycle, the values prior to 8:51:47 do not meet the VPC conditions andconsequently cannot be used for this calculation. The valuescorresponding from 8:51:47 to 8:52:07 are the first five values thatcomply with these conditions. For the second permeation cycle the sameprocedure is used. The value of R_(ab) is calculated as follows:R _(ab)(cycle 1)=(2.73*10¹²+2*2.79 10¹²+2.81*10¹²+2.83*10¹²)/5=2.79*10¹²m⁻¹R _(ab)(cycle 2)=(2.80*10¹²+2.8310¹²+2.90*10¹²+2.97*10¹²+2.98*10¹²)/5=2.89*10¹² m⁻¹ΔR _(ab) =R _(ab)(cycle 2)−R _(ab)(cycle 1)=2.89*10¹²−2.79*10¹²=1.0*10¹¹m⁻¹

As it has been previously established, R_(c) can be estimated asfollows:R _(c) =M(T ₂ −T ₁)

After performing the linear regression the value of the slope M wasdetermined:M=1.18*10¹¹ m⁻¹/minR _(c)=1.18*10¹¹ m⁻¹/min*(9.99 min)=1.17*10¹² m⁻¹

From the analysis of the calculated resistances it can be concluded thatthe values of ΔR_(ab) and R_(c) are both higher than the correspondingsustainable set points, which means that the system is operating at theRed Zone and the application of the Fouling Removal mode is needed toavoid any slugging of the membrane modules.

Following the established control hierarchy for the Fouling Removalmode, the aeration frequency factor was increased from 0.25 (10 on/30off) to 0.5 (10 on/10 off) while maintaining the remaining operationalparameters constant. Immediately after the change in aeration frequencyfactor, the operational data corresponding to these new operationalconditions was used to calculate the different relevant resistances toassess the effectiveness of these measures on fouling removal. Theprocedure is the same as done previously.

Tables 1.3 and 1.4 contain some of the operational data corresponding totwo consecutive permeation cycles performed on the Energy Savings modeusing an aeration frequency factor of 0.25. These operational data willbe used to describe the resistance calculations.

TABLE 1.3 Permeation cycle 1 experimental data (experimental conditions2). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 00:05:42 00.016 14.256 0.001 N/A N/A 00:05:47 0 −0.109 14.260 0.001 N/A N/A00:05:52 0 0.003 14.244 0.001 N/A N/A 00:05:57 0 0.011 14.240 0.001 N/AN/A 00:06:02 0 0.019 14.214 0.001 N/A N/A 00:06:07 0 −0.109 14.206 0.001N/A N/A 00:06:12 0 0.003 14.191 0.001 N/A N/A 00:06:17 0 0.011 14.1760.001 N/A N/A 00:06:22 0 −0.006 14.160 0.001 N/A N/A 00:06:27 0 −0.02914.149 0.001 N/A N/A 00:06:32 0 −0.001 14.160 0.001 N/A N/A 00:06:37 00.005 14.153 0.001 N/A N/A 00:06:42 6.350 0.850 14.157 0.001 1.940E+12−1.68 00:06:47 14.466 1.886 14.157 0.001 1.908E+12 −3.77 00:06:52 15.1171.900 14.160 0.001 1.839E+12 0.29 00:06:57 15.154 1.910 14.153 0.0011.844E+12 −0.46 00:07:02 15.264 1.920 14.153 0.001 1.836E+12 1.3700:07:07 15.117 1.921 14.153 0.001 1.861E+12 −0.01 00:07:12 15.147 1.92914.157 0.001 1.861E+12 −0.01 00:07:17 15.213 1.930 14.179 0.0011.861E+12 −1.11 00:07:22 15.271 1.919 14.199 0.001 1.840E+12 1.2100:07:28 15.227 1.936 14.210 0.001 1.863E+12 −0.78

TABLE 1.4 Permeation cycle 2 experimental data (experimental conditions2). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 00:16:470.000 0.011 14.286 0.001 N/A N/A 00:16:52 0.000 −0.037 14.275 0.001 N/AN/A 00:16:57 0.000 0.007 14.267 0.001 N/A N/A 00:17:02 0.000 0.03514.256 0.001 N/A N/A 00:17:07 0.000 0.008 14.233 0.001 N/A N/A 00:17:120.000 0.042 14.221 0.001 N/A N/A 00:17:17 0.000 0.004 14.210 0.001 N/AN/A 00:17:22 0.000 −0.023 14.199 0.001 N/A N/A 00:17:27 0.000 0.00214.172 0.001 N/A N/A 00:17:32 0.000 −0.011 14.172 0.001 N/A N/A 00:17:370.000 0.007 14.172 0.001 N/A N/A 00:17:42 0.000 0.777 14.153 0.001 N/AN/A 00:17:47 7.398 1.020 14.160 0.001 1.987E+12 −7.99 00:17:52 15.0731.893 14.172 0.001 1.840E+12 0.75 00:17:57 15.073 1.912 14.160 0.0011.854E+12 −0.17 00:18:03 15.198 1.915 14.137 0.001 1.851E+12 0.5700:18:08 15.183 1.931 14.141 0.001 1.861E+12 0.04 00:18:13 15.154 1.93114.160 0.001 1.862E+12 −0.43 00:18:18 15.235 1.930 14.160 0.0011.854E+12 0.29 00:18:23 15.249 1.935 14.179 0.001 1.859E+12 −0.1200:18:28 15.227 1.939 14.191 0.001 1.857E+12 −1.32 00:18:33 15.396 1.93314.199 0.001 1.833E+12 −0.26 00:18:38 15.344 1.911 14.202 0.0011.828E+12 1.90 00:18:43 15.161 1.929 14.206 0.001 1.864E+12 −0.25

The operational data corresponding to these new conditions was used tocalculate the different relevant resistances.

As mentioned before, the resistance after backwash for both permeationcycles will be calculated based on the average of the first five valuesthat comply with the valid permeation cycle conditions. As it can beobserved from the above table; the values prior to 0:06:47 do not meetthe VPC conditions and consequently cannot be used for this calculation.The values corresponding from 0:06:47 to 0:07:07 are the first fivevalues that comply with these conditions. For the second permeationcycle the same procedure is used. The value of R_(ab) is calculated asfollows:R _(ab)(cycle1)=(1.908*10¹²+1.839*10¹²+1.844*10¹²+1.836*10¹²+1.861*10¹²)/5=1.857*10¹²m⁻¹R _(ab)(cycle2)=(1.840*10¹²+1.854*10¹²+1.851*10¹²+1.861*10¹²+1.862*10¹²)/5=1.853*10¹²m⁻¹ΔR _(ab) =R _(ab)(cycle 2)−R _(ab)(cycle 1)=1.853*10¹²−1.857*10¹²=−4*10⁹m⁻¹

As it has been previously established, R_(c) can be estimated asfollows:R _(c) =M(T ₂ −T ₁)

After performing the linear regression the value of the slope M wasdetermined:M=1.65*10¹⁰ m⁻¹/minR _(c)=1.65*10¹⁰ m⁻¹/min*(9.99 min)=1.648*10¹¹ m⁻¹

From the analysis of the calculated resistances it can be concluded thatthe values of ΔR_(ab) and R_(c) are both lower than their correspondingoptimized set points, which means that the system is operating at theGreen Zone and the application of the Energy Savings mode is availableto reduce the MBR operational costs.

Following the established control hierarchy for the Energy Savings mode,the relaxation period duration was decreased from 1 min to 30 secondsand the net permeate flux was increased from 14 to 16 gfd by increasingthe instantaneous flux from 15.4 to 17.6 gfd, while maintaining theremaining operational parameters constant. Optionally, the net permeateflux alone could have been adjusted as was done in the long term testsdescribed further below. Although the control hierarchy gives a higherpreference to other operational changes such as the decrease of theaeration frequency factor or aeration flow and the increase of thepermeation cycle duration, these changes were difficult to implement dueto intrinsic limitations of the MBR system in this example.

Immediately after the change in relaxation period duration and increasein net permeate flux, the operational data corresponding to these newoperational conditions was used to calculate the different relevantresistances to assess the effectiveness of these measures on maintainingstable operational conditions while allowing for MBR operational costssavings. The procedure is the same as done previously.

Tables 1.5 and 1.6 contain some of the operational data corresponding totwo consecutive permeation cycles of operation under the above describedoperational conditions. These operational data will be used to describethe resistance calculations.

TABLE 1.5 Permeation cycle 1 experimental data (experimental conditions3). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 00:09:450.000 0.068 17.803 0.001 N/A N/A 00:09:50 0.000 −0.070 17.800 0.001 N/AN/A 00:09:55 0.000 0.013 17.800 0.001 N/A N/A 00:10:00 0.000 0.02017.800 0.001 N/A N/A 00:10:05 0.000 0.044 17.796 0.001 N/A N/A 00:10:100.000 −0.112 17.800 0.001 N/A N/A 00:10:15 6.607 0.808 17.788 0.0011.984E+12 4.781757 00:10:20 15.564 2.227 17.784 0.001 2.084E+12 −7.6926300:10:25 16.956 2.284 17.765 0.001 1.935E+12 1.196952 00:10:30 16.9482.270 17.769 0.001 1.959E+12 1.201163 00:10:35 16.846 2.282 17.762 0.0011.982E+12 −0.05997 00:10:40 16.941 2.296 17.750 0.001 1.981E+12 0.57332700:10:45 16.941 2.306 17.750 0.001 1.993E+12 0.402593 00:10:50 16.9192.313 17.758 0.001 2.001E+12 0.783189 00:10:56 16.890 2.323 17.769 0.0012.017E+12 −1.33901 00:11:01 17.117 2.334 17.765 0.001 1.990E+12 0.34757800:11:06 16.919 2.296 17.765 0.001 1.997E+12 −0.7252 00:11:11 17.0882.301 17.777 0.001 1.982E+12 −0.55843 00:11:16 16.985 2.292 17.773 0.0011.971E+12 −0.22469 00:11:21 17.029 2.287 17.773 0.001 1.967E+12 −0.1716800:11:26 17.190 2.305 17.773 0.001 1.964E+12 2.277346 00:11:31 16.8902.317 17.758 0.001 2.009E+12 0.058487

TABLE 1.6 Permeation cycle 2 experimental data (experimental conditions3). FLUX TMP Temp Rt Time (gfd) (psi) (° C.) μ (m⁻¹) dR/dT 00:20:190.000 0.048 17.765 0.001 N/A N/A 00:20:24 0.000 0.082 17.773 0.001 N/AN/A 00:20:29 0.000 0.010 17.769 0.001 N/A N/A 00:20:34 0.000 0.02117.773 0.001 N/A N/A 00:20:39 0.000 0.030 17.762 0.001 N/A N/A 00:20:440.000 0.004 17.754 0.001 N/A N/A 00:20:49 6.658 0.838 17.750 0.0011.971E+12 7.160825 00:20:54 15.125 2.175 17.746 0.001 2.124E+12 −11.053700:20:59 17.139 2.259 17.731 0.001 1.912E+12 1.299441 00:21:04 17.1242.262 17.712 0.001 1.937E+12 0.266417 00:21:09 17.190 2.286 17.708 0.0011.943E+12 1.798625 00:21:14 17.007 2.291 17.704 0.001 1.978E+12 0.1819900:21:19 17.022 2.315 17.704 0.001 1.982E+12 −0.7813 00:21:24 17.2052.312 17.712 0.001 1.966E+12 1.718922 00:21:29 17.007 2.321 17.720 0.0012.001E+12 1.289662 00:21:34 16.861 2.337 17.712 0.001 2.027E+12 −3.7109400:21:39 16.956 2.407 17.697 0.001 1.954E+12 0.680588 00:21:44 17.1392.313 17.712 0.001 1.968E+12 0.559187 00:21:49 16.861 2.308 17.704 0.0011.979E+12 1.170542 00:21:54 16.802 2.299 17.712 0.001 2.002E+12 −0.8916300:22:00 16.970 2.310 17.697 0.001 1.985E+12 1.07242 00:22:05 16.9412.322 17.697 0.001 2.006E+12 1.359174 00:22:10 16.787 2.333 17.697 0.0012.034E+12 −1.23236 00:22:15 17.036 2.342 17.689 0.001 2.009E+12 −0.00051

The operational data corresponding to these different operationalconditions was used to calculate the different relevant resistances.

As mentioned before, the resistance after backwash for both permeationcycles will be calculated based on the average of the first five valuesthat comply with the valid permeation cycle conditions. As it can beobserved from the above table; for the first permeation cycle the valuesprior to 0:10:20 do not meet these requirements and consequently cannotbe used for this calculation. The values corresponding from 0:10:20 to0:10:40 are the first five values that comply with these conditions. Forthe second permeation cycle the same procedure is used. The value ofR_(ab) is calculated as follows:R _(ab)(cycle1)=(2.084*10¹²+1.935*10¹²+1.959*10¹²+1.982*10¹²+1.981*10¹²)/5=1.988*10¹²m⁻¹R _(ab)(cycle2)=(2.124*10¹²+1.912*10¹²+1.937*10¹²+1.943*10¹²+1.978*10¹²)/5=1.979*10¹²m⁻¹ΔR _(ab) =R _(ab)(cycle 2)−R _(ab)(cycle 1)=1.979*10¹²m⁻¹−1.988*10¹²=m⁻¹=−9.00*10⁹ m⁻¹

As it has been previously established, R_(c) can be estimated asfollows:R _(c) =M(T ₂ −T ₁)

After performing the linear regression the value of the slope M wasdetermined:M=1.67*10¹⁰ m⁻¹/minR _(c)=1.67*10¹⁰ m⁻¹/min*(9.99 min)=1.66*10¹¹ m⁻¹

From the analysis of the calculated resistances it can be concluded thatthe values of ΔR_(ab) and R_(c) are both lower than their correspondingsustainable set points, which means that the system is operating at theGreen Zone and the application of the Energy Savings mode is availableto reduce the MBR operational costs, while maintaining sustainableoperational conditions in the system.

FIG. 4 presents the long term results obtained from testing using asystem similar to that above except that dR/dT was not used as a VPCcondition and A.F.F. was the only controlled parameter. FIG. 3 shows thenet permeate flux with the obtained cake resistance for each permeationcycle along with the values of the sustainable and optimized cakeresistance, respectively. During this testing, frequent peak flux eventswere simulated by increasing the net permeate flux from 16 up to 26 gfd.These peak flux events were performed to assess the sensitivity of theon-line process control system when faced with operational disturbances.

As it can be observed in FIG. 4, as the net permeate flux increased from16 gfd to 26 gfd the cake resistance also increased. During some of thepeak flux events the obtained values of cake resistance exceeded thesustainable cake resistance set point value causing the system tooperate in Fouling Removal mode hence using an aeration frequency factorof 0.5.

However, during most permeation cycles the obtained values of cakeresistance were around the optimized set point allowing the system tooperate in the Energy Savings mode hence using an aeration frequencyfactor of 0.25. The obtained values of ΔR_(ab) were used but are notpresented in FIG. 4. The observed trend of ΔR_(ab) was very similar tothat of the cake resistance. Table 1.7 presents the percentage andnumber of cycles performed at each aeration frequency factor.

TABLE 1.7 On-line process control system long term testing summary.A.F.F Aeration Frequency % Performed Number of Cycles 0.25 10 ON/30 OFF87 4007 0.5 10 ON/10 OFF 13 602

As it can be observed in Table 1.7 for the vast majority of thepermeation cycles the air consumption was reduced by 50% by prolongingthe OFF time from 10 up to 30 seconds, which led to a significantreduction in the energy requirements of the MBR system.

The invention claimed is:
 1. A process for operating an immersedmembrane filtration system, the immersed membrane filtration systemcomprising a plurality of membrane cassettes divided into trains, eachtrain containing a sub-set of the membrane cassettes connected togetherfor permeate removal, and a control system, the process comprising astep of: altering the operation of the system considering real-timeprocess information according to a hierarchy including the followingparameters: a. the relative duration of a higher flow rate and a lowerflow rate of a gas that produces bubbles from under or between themembranes; and one or both of, b. the on and off production times for anindividual train, the selectable off production times including times ofat least one hour in duration; and, c. the number of trains inoperation, wherein the selectable number of trains in operation includes(a) all trains in the system or (b) at least one but less than alltrains in the system, wherein one parameter in the hierarchy is changedto a different discrete state before changing another parameter in thehierarchy.
 2. The process of claim 1 wherein the process informationincludes one or more of: a. permeate flow b. reject or concentrateflowrate c. sludge filterability d. fluid temperature e. fluid viscosityf. fluid suspended solids concentration g. coagulant or otherpretreatment chemical addition rate h. activated carbon dosage rate orconcentration i. blower inlet air temperature j. recovery rate (permeateproduced/feedwater provided) k. dissolved oxygen concentration l. oxygenuptake rate m. sludge retention time n. mixed liquor recirculationrates.
 3. The process of claim 1 wherein the control system comprises aprogrammed controller that operates the immersed membrane filtrationsystem at a fixed aeration rate for a period of time to establishbaseline process operating conditions including permeate flow andtransmembrane pressure, then automatically conducts a test in which itadjusts the aeration rate upward or downward by a certain percentage andthen determines the effect of the aeration rate change on the baselineprocess operating conditions, then the control system assesses if thenew aeration rate yields better performance in terms of either actualflow rate or flowrate per unit energy input, and based on the results ofthe test, the control system chooses another aeration rate to beevaluated, then this test is repeated until an optimum aeration rate hasbeen determined.
 4. The process of claim 1 wherein the control systemcomprises a programmed controller that conducts a test to determine therate of increase of transmembrane pressure with time without membraneaeration occurring, the test including an initial aeration withoutpermeation step followed by a permeation without aeration step duringwhich transmembrane pressure is monitored, then a second aerationwithout permeation step follows during which the majority of thematerial deposited on the membrane during the earlier step is removed,then the control system correlates the transmembrane pressure increasewith time against a previously input model to determine an optimumaeration rate and operating strategy.
 5. The process of claim 1 whereinthe process information is used to calculate a resistance value.
 6. Theprocess of claim 1 wherein the hierarchy includes: the number of trainsin operation.